ABSTRACT Cancer patients often relapse because their tumors contain drug-resistant cells, which though initially present at small fractions, become enriched during treatment to yield incurable tumors. Traditional approaches to isolate and study drug-resistant cells can require months of labor-intensive work, which is both cost- and time- prohibitive during the early stages of drug development. Single cell array technologies are well suited for measuring functional cellular properties; however, existing market offerings cannot achieve the massive scales required to identify rare drug-resistant cells that are present at frequencies as low as 0.002% ? 0.1% (i.e., 1 drug-resistant cell for every 1,000 to 50,000 drug-sensitive cells). Although the latest single cell genomic techniques can achieve the required throughput, they do not allow for identification of subclones with functional properties of interest (i.e., resistance to a drug). Celldom aims to solve this problem by developing a single cell analysis workflow that combines image-based phenotyping and gene expression analysis at scales of up to 105 single cells organized in a standard well plate footprint. At these scales, it is possible for drug companies and clinicians to pinpoint better lead candidates with more attractive resistance profiles and provide improved clinical outcomes. Building on a recently completed Phase I project, in which we demonstrated the phenotyping capabilities ? specifically, the ability to organize and track single cell clones over multiple days ? here, we propose to add RNA-seq capabilities to realize a comprehensive `multi-omics' single cell analysis platform with high resolution live cell imaging. Our collective workstream is described in the following two specific aims. In Aim 1, we will synthesize an array of DNA barcodes inside Celldom's microfluidic chips, show that the barcodes can capture human AML cell lysates, demonstrate cDNA synthesis, and retrieve molecules for NGS. In Aim 2, we will capture a mixture of murine NIH 3T3 and human AML cells in single cell per trap format, identify the species of trapped cell with imaging, then demonstrate the scRNA-seq workflow. Successful completion of this Phase I study will de-risk our platform by proving the principle that we can conduct both drug-resistance testing and genomic analysis at the scale of tens of thousands of single cells per chip. In Phase II, we will optimize the platform for launch, first as drug discovery research tool for academic and industrial customers, and later as a companion diagnostic for evaluating clinical therapies.